1. Field of the Invention
The present invention relates generally to radiation therapy, and more specifically to conformal radiation therapy of tumors, and particularly to a radiation therapy treatment planning system, methods, and apparatus for conformal radiation therapy.
2. Description of Related Art
Modern day radiation therapy of tumors has two goals: eradication of the tumor, and avoidance of damage to healthy tissue and organs present near the tumor. It is known that a vast majority of tumors can be eradicated completely if a sufficient radiation dose is delivered to the tumor volume; however, complications may result from use of the necessary effective radiation dose, due to damage to healthy tissue which surrounds the tumor, or to other healthy body organs located close to the tumor. The goal of conformal radiation therapy is to confine the delivered radiation dose to only the tumor volume defined by the outer surfaces of the tumor, while minimizing the dose of radiation to surrounding healthy tissue or adjacent healthy organs.
Conformal radiation therapy has been traditionally approached through a range of techniques, and typically uses a linear accelerator (“LINAC”) as the source of the radiation beam used to treat the tumor. The linear accelerator typically has a radiation beam source, which is rotated about the patient and directs the radiation beam toward the tumor to be treated. The beam intensity of the radiation beam is a pre-determined, constant beam intensity. Multi-leaf collimators, which have multiple leaf or finger projections that can be moved individually into and out of the path of the radiation beam, can be programmed to follow the spatial contour of the tumor as seen by the radiation beam as it passes through the tumor, or the “beam's eye view” of the tumor during the rotation of the radiation beam source, which is mounted on a rotatable gantry of the LINAC. The multiple leaves of the multi-leaf collimator form an outline of the tumor shape as presented by the tumor volume in the direction of the path of travel of the radiation beam, and thus block the transmission of radiation to tissue disposed outside the tumor's spatial outline as presented to the radiation beam, dependent upon the beam's particular radial orientation with respect to the tumor volume.
Another approach to conformal radiation therapy involves the use of independently controlled collimator jaws, which can scan a slit field across a stationary patient at the same time that a separate set of collimator jaws follows the target volume as the gantry of the linear accelerator rotates. An additional approach has been the use of attachments for LINACs, which allow a slit to be scanned across the patient, the intensity of the radiation beam in the entire slit being modified as the slit is being scanned.
A further approach for conformal radiation therapy treatment has been the use of a narrow pencil beam of high energy photons, whose energy can be varied. The beam is scanned over the tumor target volume so as to deliver the best possible radiation dose distribution in each orientation of the gantry upon which the photon beam source is mounted.
A major problem associated with such prior art methods of conformal radiation therapy are that if the tumor volume has concave borders, or surfaces, varying the spatial configuration, or contour, of the radiation beam, is only successful part of the time. In particular, when the convolutions, or outer surfaces, of a tumor are re-entrant, or concave, in a plane parallel to the path of the radiation treatment beam, the thickness of the tumor varies along the path of the radiation beam, and healthy tissue or organs may be disposed within the concavities formed by the outer tumor concave surfaces.
In order to be able to treat tumors having concave borders, it is necessary to vary the intensity of the radiation beam across the surface of the tumor, as well as vary the outer configuration of the beam to conform to the shape of the tumor presented to the radiation beam. The beam intensity of each radiation beam segment should be able to be modulated to have a beam intensity related to the thickness of the portion of the tumor through which the radiation beam passes. For example, where the radiation beam is to pass through a thick section of a tumor, the beam intensity should be higher than when the radiation beam passes through a thin section of the tumor.
Dedicated scanning beam therapy machines have been developed wherein beam intensity modulation can be accomplished through the use of a scanning pencil beam of high-energy photons. The beam intensity of this device is modulated by increasing the power of its electron gun generating the beam. The power increase is directed under computer control, as the gun is steered around the tumor by moving the gantry upon which it is mounted and the table upon which the patient lies. The effect is one of progressively “painting” the target with the thickness, or intensity, of the paint (radiation beam intensity) being varied by the amount of paint on the brush (amount of power applied to the electron gun) as the electron gun moves over the tumor. Such dedicated scanning beam therapy machines, which utilize direct beam energy modulation, are expensive and quite time consuming in their use and operation, and are believed to have associated with them a significant patient liability due to concerns over the computer control of the treatment beam.
Other methods and apparatus for conformal radiation therapy have been developed that spatially modulate the beam intensity of a radiation beam across a volume of tissue in accordance with the thickness of the tumor in the volume of tissue by utilizing a plurality of radiation beam segments. Such methods and apparatus utilize attenuating leaves, or shutters, in a rack positioned within the radiation beam before the beam enters the patient. The tumor is exposed to radiation in slices, each slice being selectively segmented by the shutters.
The foregoing methods and apparatus were designed to minimize the portion of the structures being exposed to radiation. However, because exposure to surrounding structures cannot be completely prevented, treatment plans are desired that are optimized to eradicate the tumor volume while minimizing the amounts of radiation delivered to the surrounding structures. Existing methods and apparatus for optimizing treatment plans use a computer to rate possible plans based on score functions which simulate a physician's assessment of a treatment plan.
Existing methods and apparatus utilize a computational method of establishing optimized treatment plans based on an objective cost function that attributes costs of radiation of various portions of both the tumor and surrounding tissues, or structures. One such computational method is known in the art as simulated annealing. Existing simulated annealing methods utilize cost functions that consider the costs of under-exposure of tumor volumes relative to over-exposure of surrounding structures. However, the cost functions used in existing methods generally do not account for the structure volumes as a whole, relying merely on costs related to discrete points within the structure, and further, generally do not account for the relative importance of varying surrounding structure types. For example, certain structure types are redundant in their function and substantial portions of the structure volume can be completely eradicated while retaining their function. Other structure types lose their function if any of the structure is completely eradicated. Therefore, the more sensitive structure volumes can receive a measured dose of radiation so long as no portion of the structure is subjected to a lethal dose.
Existing cost functions utilized in the optimization of treatment plans traditionally have not accounted for such varying costs associated with the different types of structures. After the treatment plan is optimized, the physician must evaluate each computed treatment plan for compliance with the desired treatment objective. If the computed treatment plan does not successfully meet the treatment objectives, the optimization process is repeated until a treatment plan can be computed that meets the physician's treatment objectives for both the tumor volume and the surrounding structures. Further, existing methods and apparatus traditionally have not allowed the physician to utilize the familiar partial volume data associated with Cumulative Dose Volume Histogram (“CDVH”) or dose volume histograms (“DVH”) curves in establishing the desired dose distributions.
A method and apparatus for determining an optimized radiation beam arrangement for applying radiation to a tumor target volume while minimizing radiation of a structure volume in a patient is disclosed in U.S. Pat. No. 6,038,283, entitled “Planning Method and Apparatus for Radiation Dosimetry, commonly assigned with the present application, and incorporated herein by reference.” The method and apparatus uses an iterative cost function based on a comparison of desired partial volume data, which may be represented by CDVHs or DVHs.
Another method and apparatus for determining an optimized radiation beam arrangement for applying radiation to a tumor target volume while minimizing radiation of a structure volume in a patient is disclosed in U.S. Pat. No. 6,393,096, entitled “Planing Method and Apparatus for Radiation Dosimetry.”
Many of the foregoing systems replace the traditional forward planning methodology. With forward planning, the user starts by specifying the direction of the beams and their intensities and the computer determines the dose calculations, shows the user what is obtained, and then, based upon to what extent the goals are met, the user goes back and changes the beam parameters. The foregoing systems utilize inverse planning. In an inverse planning system, a professional/user starts with the goals he or she wants to achieve, specifies a prescription for the patient as to how much dose the user would like the tumor to get, and to what degree to spare the other healthy tissue. The computer then calculates all of the various treatment plan parameters, i.e., the direction and corresponding intensity of the beam as it is applied from the different directions. Basically, in inverse planning, the user starts with the clinical goals and lets the computer determine the beam intensities, whereas, in a forward planning system, the user starts with the beam layouts and basically assesses the effectiveness of the plan relative to the goals, and iterates them that way.
In the foregoing system, the user starts from a computerized tomographic (“CT”) scan or a magnetic resonance imaging (“MRI”) scan. From the CT scan, for example, the user identifies tissue anatomically, typically slice-by-slice, separating what the user wants treated from that which the user wants to spare. For example, the user may identify one item as a tumor, another as the prostate, another as the bladder, etc. Generally, the user will use a pointing device, or mouse, to draw around the area the physician wants to treat in each of a number of slices, since the CT scans provide a set of serial slices of the patient's body. This process can be time-consuming. It would be advantageous, if the tumor is very well differentiated in the CT scan or whatever other image the user selected to examine the tumor, the user could employ an automated tool to allow the user to just “click” on the tumor or target, and automatically determine and mark the location of the boundaries of the tumor.
DVH curves have been used as a prescription and as a feedback mechanism, whereby. the user specifies goals in terms of such DVH curves. The DVH curves represent a summary of how much dose the individual structures are getting. For example, the user may specify the desire for the target to receive a certain minimum dose level delivered to 80% of the target, and also a certain minimum dose level delivered to 90% of the target, as a representation of how the user believes a tumor or target needs to be treated. The computer then develops a treatment plan. After the computer has actually determined how to treat the patient, DVHs are the mechanism for summarizing that treatment and for review by the user. For example, the user requests a specific curve, and the computer then displays the actual curve at the derived treatment plan. The use of the DVH curve in this manner is a familiar, common way of representing such information for plan evaluation by a physician.
To define the DVH prescription, the user typically starts with either a graphical depiction and drags points on a graph on the screen, or enters numbers in the text field boxes. Either way, the user defines the DVH curve. The result is essentially a wish list—a hope that the user can achieve this kind of a DVH curve. After the user completes the proposed DVH curves, the prior systems enter an optimization process that is independent of further user input. This process may typically take at least 10 minutes. The result of the calculations is the return of all of the different “wishes,” which may or may not have all been achievable, into an actual plan for treatment. The DVH curves, representing the volumetric statistics of a plan processed by a computer, however, are not manipulatable. It would be advantageous to provide direct manipulation of volumetric to statistics.
DVH curves are a way of summarizing the dosimetric properties of a plan. After inverse planning optimization, the user typically examines the actual DVH curves of the optimized plan. The user can compare DVH curves actually achieved with DVH prescriptions to decide if the developed treatment plan was satisfactory. What is satisfactory may be a question of (1) whether enough dose is getting to enough of the tumor, (2) whether too much dose is getting to some parts of the tumor, and/or (3) how much dose is getting to the healthy structures not identified as tumor. All tissue (target and structures) that can be represented is summarized individually on DVH curves. For example, if the tumor was located in the prostate, the user would be typically provided a single curve on the graph for the prostate, another curve for the bladder, and so on.
One can draw the same conclusions summarized in the DVH curves by actually looking at the CT slices to see the result in more detail. The CT scan slices typically have an overlay showing the various levels of dose applied to discrete portions of each slice. That is, the user can draw conclusions based upon the level of dose applied to any specific organ of interest. In a planning system distributed by NOMOS Corporation, the assignee of the present application under the trademark CORVUS®, the dose in the individual slices is depicted through the use of isodose curves drawn on the CT scan slice. Isodose curves are visually like a contour map of different usually colored lines representing a specified dose level, e.g., 50 Gy, wherein everything inside of the particular curve would be getting at least 50 Gy.
It would be advantageous to decrease the amount of time involved in deciding upon a given treatment plan. Any particular patient might have two or three different treatment plans determined before the user finds a plan believed to be the best. It would also be advantageous if these systems provided the user a more intuitive direct control over what is happening within the plan optimization process that is easier for the user to appreciate.
Traditionally, DVH curves were only used as a form of plan evaluation tool; however, some of the foregoing systems involve drawing DVH curves ahead of time—the users must initially determine the desired goals. It would be advantageous for a computer system to immediately display the user's request and correspondingly display what the planning system can achieve. It would be advantageous for the planning system, if there are compromises to be made between different goals, to display them to the user in a dynamic, interactive manner, and permit the user to dynamically edit the goals and change the terms in which the user would specify a prescription. It would further be advantageous to provide dynamic constraint balancing, i.e., a real-time system for adjusting dosimetric goals while viewing at least one representation of dose in the patient.
Radiation treatment planning includes balancing various, often mutually exclusive, goals. Once these goals are represented, the treatment planning system must know what their relative priorities are in order to balance them optimally. Many current treatment planning systems require the user to explicitly prioritize goals, which may be a difficult, imprecise, and potentially time-consuming process. For example, in “a perfect world,” the user may require an entire prostate target to receive 50 Gy, with correspondingly no dose at all to the rectum located 1 millimeter away. This task is virtually physically impossible. So, the issue becomes balancing those two goals and determining which goal is more important than the other. Treatment plans have previously required the user to specify prioritization ahead of time. In some systems, part of what the user is doing when entering DVH curves, is to set priorities between dosing target at a very high level and sparing an organ at risk (“OAR”). Developing such priorities may be a difficult and time-consuming task for the treatment planner. It would thus be an advantage to minimize the need for user-implemented prioritization.
The Applicant has recognized that there are two characteristics that can eliminate the need for user implemented prioritization: First, during interaction with the computer system, an algorithm can effectively consider the user's last input to be the most important requirement. Second, the user can choose to undo the prior input to whatever extent desired. For example, if the user decides to remove, or minimize, a dose from a structure, then priority-wise, that action is the most important requirement. The user may then realize the consequences of that prioritization and may back off on its importance by partially undoing it. This dual prioritization concept is implicit in the interactive process. A computer system and associated algorithms, however, requires an understanding of the relationship of these different goals. As the user layers new goals on top of old goals, the system needs to know how those goals should be balanced. It would be advantageous to provide automatic constraint weighing, i.e., a level of interactivity that in turn permits the prioritization to be inferred from user actions and a sequence of user inputs in the form of plan adjustments rather than direct entry of such priorities; the ultimate result being the elimination from the user's experience the idea of such priorities.
Prior planning systems generally require the user to make adjustments to a patient treatment plan in one of two ways: change delivery parameters (e.g., beam direction and size); or change volumetric dosage goals. It would, therefore, be advantageous to provide for real-time, direct manipulation of isodose contour lines on an isodose plot on a tomographic scan. It would also be advantageous to provide a planning system that allows direct manipulation of deliverable DVH curves rather than indirect specification of potentially impossible, idealized prescriptions.
To some extent, radiation therapy treatment planning is still an art of balance and compromise. It would be advantageous to provide a partial “undo of changes function” to aid a user, wanting to make a plan variation, in the discovery of what sacrifices that a particular change requires. It would correspondingly be advantageous to provide the user with a real-time control permitting the user to dynamically undo a change, completely or partially, and to explore trade-offs, in order to quickly select an optimum balance.
Since developing a radiation therapy treatment plan is an exploration of these trade-offs and other possibilities, some treatment planning systems have shown benefits in providing a means for saving several iterations of a plan for subsequent comparison, and to permit “backtracking.” It would therefore be advantageous to provide the user a real-time control permitting the user to establish any two of these plan “checkpoints” as the endpoints on a single continuum and it would be a further advantage to provide the user a means to interpolate between the checkpoints to extract a new version for further comparison or implementation.
In order to interoperate most effectively with other systems, it would be desirable that a new system capable of flexible adjustments, such as that of the present invention, be able to automatically generate treatment goals in its own formulation that would produce a treatment plan identical to one created by another system. This feature would permit a new system to “carry forward” and adjust treatment plans created by other systems. It would thus be advantageous to provide a system with an optimized prescription match function that implements an algorithm, which develops the appropriate treatment goals and their corresponding weights.
In order to permit real-time interactive plan adjustments on current generation computer hardware, the objective function, which the computer frequently optimizes, must be restated in a way which is compatible with rapid optimization without significant reductions in capability. One methodology is to reformulate the goals such that each contributor to the objective function is monotonic in its first derivative. Optimization with monotonic first derivatives of objective contributors basically refers to influence functions, or the terms in a cost function, and it provides a mathematical class of those functions that permits certain computer systems to work calculations quickly. Each objective contributor is formulated in terms of a function of dose. By specifying that the derivative of that function is monotonic, so that the derivative is always either increasing, decreasing, or not changing, never starting out increasing and then decreasing, one can enable a different class of optimization. It would therefore be advantageous to provide a system that utilizes optimization with monotonic first derivatives of objective contributors.
Computing the objective function may be done by effectively sampling the CT or other image of the patient in a number of places to try to capture all of the important aspects of the treatment plan. Speed and interactivity can be improved through use of sampling, which identifies a smaller number of points within the patient at which to simulate the treatment dose. These points must be distributed sufficiently such that the software is “aware” of all important dose features; however, as performance is inversely proportional to the number of such points, one wants to identify the smallest possible group that meets that criteria. It would, therefore, be advantageous to provide a computer system that has an algorithm for automatic selection of minimal plan evaluation points.
A Fluence map is a spatial map of how the radiation is being delivered through a particular position of the delivery device. Plan delivery mechanisms often require that beam fluences take on specific discrete values, whereas optimizers can work in either discrete or continuous space. It would therefore be advantageous to provide an apparatus for converting an optimized plan into a deliverable discrete one.
Different radiation delivery devices will have different constraints upon what they can actually do. For example, one might be able to adjust beamlets that are just a few millimeters across, and one may have to make adjustments that are larger, a centimeter or more across. Another constraint is the degree of variation within a fluence map. For example, the plan map may require 100% of the beam in the middle of the beam to be passing through, and only 50% of the beam in a particular portion to be passing through. Mode fold discretization is a methodology of designing the fluence maps to make the best use of the equipment. Historically, fluence maps are constrained to have certain levels, such as 10% steps, i.e., the delivery device can have a 50% transparency at one point, but not a 52% transparency. These constraints limit the treatment plans that the user can develop. Mode fold discretization assesses a given treatment plan for a patient, and if limited to a discrete number of levels, it determines which of those levels are the optimum. For example, the optimum levels may not be 10%, 20%, 30%, 40% and 50%, but instead may be 13%, 14%, 15%, 80%, and 90%. Mode fold discretization in its basic form takes a histogram of all the desired transmissions (dose levels) in the fluence map, each point representing a set of radiation levels, splits the graph at the peak levels, slides the right side over the left, and adds the overlap points. The process repeats until the algorithm has achieved a particular number of peaks corresponding to the number constrained to by the delivery equipment. Because the actual levels used can have a dramatic effect on both treatment simplicity and speed and the optimal levels for one treatment plan are typically different than those for another, it would be advantageous to provide a “mode fold” discretization algorithm which rapidly estimates the ideal fluence levels for any given treatment field.
Therefore, the art has sought a system, method and apparatus for conformal radiation therapy for treatment of a tumor which: is simple and economical to use; that has what is believed to be a high safety factor for patient safety; computes an optimal treatment plan using simple constraints and a rapid optimizer tuned to them to meet conflicting, fluid, treatment objectives of a physician, accounting for objectives in both the target tumor volume and multiple structure types; and utilizes a graphic user interface (“GUI”) displaying isodose contour maps, associated DVH curves, other statistics, and tools allowing the user to establish the desired dose distributions for each target tumor volume and tissue structure type.